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This research aims to improve ventilation management in coal mines using advanced technologies.

December 8, 2025Scientific ReportsOpen Access

Digital twin-driven deep learning prediction and adaptive control for coal mine ventilation systems

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Field validation reveals adaptive control improves ventilation systems in coal mines, indicating advanced AI methods enhance safety and efficiency.

This research aims to improve ventilation management in coal mines using advanced technologies.
Developed a digital twin framework for synchronizing physical and virtual ventilation models.
Constructed an LSTM-Attention hybrid neural network for predicting ventilation parameters.
Designed an adaptive model predictive control for optimizing fan and regulator operations.
Conducted field validation at an operational coal mine over eight months.
Achieved a mean absolute percentage error of 2.87% in parameter predictions.
Demonstrated 97.3% control accuracy and 27% reduction in energy consumption.
Achieved a 66.4% faster system response compared to conventional methods.

Xijun Yang (2025) studied this question.

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Field validation reveals adaptive control improves ventilation systems in coal mines, indicating advanced AI methods enhance safety and efficiency.